arXiv:2603.25769v1 Announce Type: cross Abstract: Large language models (LLMs) have shown promise in generating RTL code from natural-language descriptions, but existing methods
arXiv:2603.19715v1 Announce Type: new Abstract: Formal verification via interactive theorem proving is increasingly used to ensure the correctness of critical systems, yet
arXiv:2603.25768v1 Announce Type: cross Abstract: Functional verification remains a critical bottleneck in modern IC development cycles, accounting for approximately 70% of total
arXiv:2603.19828v1 Announce Type: new Abstract: Autoformalization aims to translate natural-language mathematics into compilable, machine-checkable statements. However, semantic
arXiv:2603.25719v1 Announce Type: new Abstract: We present an empirical study of how far general-purpose coding agents -- without hardware-specific training -- can optimize
arXiv:2605.06702v1 Announce Type: new Abstract: Large language models (LLMs) have become a central foundation of modern artificial intelligence, yet their lifecycle remains
arXiv:2603.22305v1 Announce Type: cross Abstract: Large Language Models (LLMs) are rapidly transitioning from static Natural Language Processing (NLP) tasks including sentiment
arXiv:2610.03984v1 Announce Type: new Abstract: Autonomous coding agents solve repository issues by reading code, running commands, editing files, and submitting patches. Extra
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